Extreme annual temperature of eighteen stations in Malaysia is fitted to the Generalized Extreme Value distribution. Stationary and non-stationary models with trend are considered for each station and the Likelihood Ratio test is used to determine the best-fitting model. Results show that three out of eighteen stations i.e. Bayan Lepas, Labuan and Subang favor a model which is linear in the location parameter. A hierarchical cluster analysis is employed to investigate the existence of similar behavior among the stations. Three distinct clusters are found in which one of them consists of the stations that favor the nonstationary model. T-year estimated return levels of the extreme temperature are provided based on the chosen models.
The issues on global warming have become very popular and been discussed both locally and internationally. This phenomenon due to the temperature rises will increase the variability of climate and more natural disasters were expected to occur. Increasing of global temperature will affect the agricultural sector, increase some of the infectious diseases that may lead to high mortality rates in humans, high demand for electricity, water and food which eventually affecting the economy of Malaysia. Hence, this work aims to study the best fitted probability distribution that describes the annual maximum temperature recorded at seventeen meteorological stations in Malaysia. The Normal, Lognormal, Gamma, Weibull and Generalized Skew Logistic distributions are considered using the maximum likelihood estimation method to estimate the parameters. The goodness of fit test and model selection criteria such as Kolmogorov-Smirnov and AndersonDarling tests, Corrected Akaike Information Criterion and Bayesian Information Criterion are used to measure the accuracy of the predicted data using theoretical probability distributions. The results show that most of the stations favour the Generalized Skew Logistic distribution as the best fitted probability distribution. Also, some stations favour the Normal, Lognormal as well as Weibull distribution as the best fitted distribution to describe the annual maximum temperature.
Extreme temperature has been carried out around the world to provide awareness and proper opportunity for the societies to prepare necessary arrangements. In this present paper, the first order Markov chain model was applied to estimate the probability of extreme temperature based on the heat wave scales provided by the Malaysian Meteorological Department. In this study, the 24-year period (1994-2017) daily maximum temperature data for 17 meteorological stations in Malaysia was assigned to the four heat wave scales which are monitoring, alert level, heat wave and emergency. The analysis result indicated that most of the stations had three categories of heat wave scales. Only Chuping station had four categories while Bayan Lepas, Kuala Terengganu, Kota Bharu and Kota Kinabalu stations had two categories. The limiting probabilities obtained at each station showed a similar trend which the highest proportion of daily maximum temperature occurred in the scale of monitoring and followed by the alert level. This trend is apparent when the daily maximum temperature data revealed that Malaysia is experiencing two consecutive days of temperature below 35˚C.
The aim of this study is to model the stock returns in Malaysia by using time series analysis to capture the characteristic of the KLCI returns. The effect of global financial crisis on the stock market in Malaysia is examined and the crisis period considered is from the beginning of 2008 until the first quarter of 2009. This study is conducted in order to identify the characteristic of the stock returns in Malaysia and whether the crisis has changed the time series model. Daily KLCI is collected from the beginning of July 2002 until the end of June 2014. The data are separated into three different periods which are the whole period (whole sample size), pre-crisis period and post-crisis period. Several tests are conducted in order to fit a suitable model for each period. The results show that the best model 70 Husna Hasan et al. for whole period and post-crisis period is the same which is ARMA(1,0)-EGARCH(1,1) model while the best model for pre-crisis period is ARMA(3,0)-EGARCH(1,1) model. The model before the global financial crisis and after the global financial crisis is different, indicating that the crisis has given the impacts on Malaysia stock market. The pre-crisis period's returns are more dependent on the previous returns. The whole period has the highest persistency and highest half-life value among all periods. The post-crisis model is found that has better forecast performances compared to whole period model.
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